Customer Relationship Management System or CRM
This technology for managing all your company’s relationships and interactions with customers and potential customers. The goal is simple: Improve business relationships. A CRM system helps companies stay connected to customers, streamline processes, and improve profitability.
When people talk about CRM, they are usually referring to a CRM system, a tool that helps with contact management, sales management, productivity, and more.
A CRM solution helps you focus on your organisation’s relationships with individual people — including customers, service users, colleagues, or suppliers — throughout your lifecycle with them, including finding new customers, winning their business, and providing support and additional services throughout the relationship.
A Customer Relationship Management System refers to the database that usually holds all customer data sources including personal data, sales opportunities, sales conversion data, revenue data, new offers and subscription renewals and many others. This platform is used as the main interface wherein the sales team reps keep all the accounts, leads, contacts, cases and all other customer focused data. Additionally, a CRM system will also hold data on several sales and marketing activities such as sales calls and event participation data which reveal more in-depth data on customers’ interests and behavior.
Marketing technology systems
Marketing technology, also known as MarTech, describes a range of software and tools that assist in achieving marketing goals or objectives. When a marketing team utilizes a grouping of marketing technologies, this is known as their marketing technology stack. MarTech has become a staple in digital marketing campaigns, but can also be used to optimize marketing efforts across any marketing channel.
A marketing technology systems can be of different types such as, email service providers, marketing automation platforms, various advertising technologies and many others, which help marketers running marketing operations and campaigns. Usually, these marketing technologies are used in sync with CRM systems as they support CRM integration to run campaigns based on the CRM data and also updates the CRM data automatically as customer data changes.
These systems also enable marketers to cross-reference their audience and customer data with a comprehensive view on customer details. These marketing systems are also used as a tool to monitor customer responses and engagement on their ongoing marketing campaigns which may or may not be related to sales offers.
Data Warehouse systems
A data warehouse system (DWH) is a data-driven decision support system that supports the decision-making process in a strategic sense and, in addition, operational decision-making, for example real-time analytics to detect credit card fraud or on-the-fly recommendations of products and services. The data warehouse provides nonvolatile, subject-oriented data that is integrated and consistent to business users on all targeted levels.
The product satisfaction and usage data is usually sent to and maintained in data warehouse systems that serve as the primary customer data repository. These customer data sources aid in different data requirements for various systems such as operations platforms, financial applications, marketing and sales systems, purchase systems and many others, since these data warehouses present cleansed, standardized and usable versions for all these different systems.
These data warehouses benefit marketers in discovering how their customers are purchasing (whether for themselves or someone in their family, friends or work), how customers are using products or services, satisfaction or issues they face, problems that products solves, and many other relevant details on usage.
Analytics applies statistical and visualization techniques that lead to valuable insights that can help the company make better business decisions.
There are gamut of analytics tools are used by marketers and sales professionals; the major types of them are reporting, data visualization, business intelligence or BI among many others. As data warehouse systems act in storing customer data diverse data sources and processes, marketers use analytics and reporting systems to process those data accumulated, visualize and format data to get insights for campaigns.
These analytics tools serve in a plethora of purposes e.g., getting generic customer trends, minute and specific insights, get data presented in visual-rich diagrams to measure and extract actionable business intelligence. That’s why these analytics tools are favorites for all marketing, advertising and sales professionals for all types of data requirements whether to source overall campaign performance insights, to delve into ad campaign data minutely based on specific metrics, to get detailed breakdown data of marketing attribution and many others.
Data Quality provides the structure necessary to have data that fulfills the needs of the business.
Very simply, if you don’t have a defined standard for quality data, how can you know if you are meeting or exceeding it? While data quality definitions as to what data quality means varies from organization to organization.
The most critical points of defining data quality may vary across industries and from organization to organization. But defining these rules is essential to the successful use of business intelligence software.
Your organization may wish to consider the following characteristics of high-quality data in creating your data quality definitions:
- Integrity: how does the data stack up against pre-established data quality standards?
- Completeness: how much of the data has been acquired?
- Validity: does the data conform to the values of a given data set?
- Uniqueness: how often does a piece of data appear in a set?
- Accuracy: how accurate is the data?
- Consistency: in different data sets does the same data hold the same value?
Data Integration (i.e. data cleansing and standardization)
data integration tools usually provide data transformation ability and could perhaps even be used to support data quality activities. The distinction between data transformation engines and data parsing and standardization tools often lies in the knowledge base that is present in the data quality tools that drive the data quality processes. Data standardization is a data processing workflow that converts the structure of different datasets into one common format of data. It deals with the transformation of datasets after the data are collected from different sources and before it is loaded into target systems. It takes a lot of time and iteration to process and results in very precise, clear, and time-consuming integration and development work.
Data standardization results from mapping the source data into a target structural representation. Customer name data provides a good example—names may be represented in thousands of semi structured forms, and a good standardizer will be able to parse the different components of a customer name (e.g., first name, middle name, last name, initials, titles, generational designations) and then rearrange those components into a canonical representation that other data services will be able to manipulate.
Data transformation is often rule based transformations are guided by mappings of data values from their derived position and values in the source into their intended position and values in the target. Standardization is a special case of transformation, employing rules that capture context, linguistics, and idioms that have been recognized as common over time through repeated analysis by the rules analyst or tool vendor.
Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets. Data governance is just one part of the overall discipline of data management, though an important one. Whereas data governance is about the roles, responsibilities, and processes for ensuring accountability for and ownership of data assets, DAMA defines data management as “an overarching term that describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.”
Data Governance provides the overarching support to data management through stewardship, policies, processes, standards, and adherence to leading practices.
Data Architecture provides the infrastructure for the storage, integration, and use of data throughout the organization. Data architectures will define a company’s livelihood. If a company were a chess piece, the data architecture defines the moves the company can make on the board. A primitive architecture allows your company to move like a pawn. An advanced architecture can make that pawn a queen.
The data architecture is 100% responsible for increasing a company’s freedom to move around the world. If agility is what is needed to avoid collapse during slow seasons or to capitalize on the spontaneous popularity of a new product, the more advanced the data architecture is, the more capable the company is to take action.
Explicitly, the data architecture:
- Gives a fuller picture of what is happening in the company
- Creates a better understanding of the company’s data
- Offers protocols by which data moves from its source to being analyzed and consumed by its destinations
- Ensures a system is in place to secure the data
- Grants all teams the ability to make data-driven decisions
Data Privacy supports the needs of the business to share data internally and externally. Data Security is a process of protecting files, databases, and accounts on a network by adopting a set of controls, applications, and techniques that identify the relative importance of different datasets, their sensitivity, regulatory compliance requirements and then applying appropriate protections to secure those resources.
Similar to other approaches like perimeter security, file security or user behavioral security, data security is not the be all, end all for a security practice. It’s one method of evaluating and reducing the risk that comes with storing any kind of data.
The Data Lifecycle follows the data throughout the company, providing integrity from the initial introduction into the company through the final deletion from the company. Data life cycle management (DLM) is a policy-based approach to managing the flow of an information system's data throughout its life cycle: from creation and initial storage to the time when it becomes obsolete and is deleted. DLM products automate the processes involved, typically organizing data into separate tiers according to specified policies, and automating data migration from one tier to another based on those criteria. As a rule, newer data, and data that must be accessed more frequently, is stored on faster, but more expensive storage media, while less critical data is stored on cheaper, but slower media.
Data Lifecycle Management itself refers to a definition and through structuring the steps that are followed by information within the company to maximize its useful life. So in this data management, Will require the use of resources that have been offered by information technology for automatic processing. Through them, it is possible to collect data for analysis and trace it to the point of storage or cleaning
Metadata is simply data about data. It means it is a description and context of the data. It helps to organize, find and understand data. Metadata allows you to use data more efficiently by providing critical information about data attributes. Metadata makes finding and working with data easier – allowing the user to sort or locate specific documents. Some examples of basic metadata are author, date created, date modified, and file size. Metadata is also used for unstructured data such as images, video, web pages, spreadsheets, etc.
Metadata can be created manually or through automation. Accuracy is increased using manual creation as it allows the user to input relevant information. Automated metadata creation can be more elementary, usually only displaying basic information such as file size, file extension, when the file was created, for example.
Metadata can be stored and managed in a database, however, without context, it may be impossible to identify metadata just by looking at it. Metadata is useful in managing unstructured data since it provides a common framework to identify and classify a variety of data including videos, audios, genomics data, seismic data, user data, documents, logs.
Data Backup and Storage
A well-known computing saying goes:
"There are two kinds of computer users. Those that have lost a major chunk of data, and those who are going to lose a major chunk of data."
Data loss can be due to natural disasters, theft, human error, and computer failure. You’ve worked to hard to collect and enter data, and you must now take care of it. The most common loss of data among students is due to “loss” of data somewhere on the computer. The best way to prevent such loss is to know the physical location of you data (local drive, removable media, network) and to use logical file names. All too often students save files to unknown locations (usually the default set up by the program) but never find saved files or have the saved files deleted by the local area network as a part of routine data cleanup. Always be aware of the location and path (“folder”) to which files are being written.
In addition, it is essential to back-up all data (e.g., data files, code books, software settings, computer programs, word processing documents). Backup systems entail manual or automated copying of files to removable media (e.g., floppy disks, Zip disks, tape) or to network storage. Backup procedures should be thoroughly tested to ensure archived files remain uncorrupted and can be restored. Procedures should be written up so that personnel unfamiliar with backup and restore methods could follow them from scratch.